def _compute_validation_outputs(self,
actions: List[List[ProductionRuleArray]],
best_final_states: Mapping[int, Sequence[GrammarBasedState]],
world: List[SpiderWorld],
target_list: List[List[str]]) -> None:
batch_size = len(actions)
action_mapping = {}
for batch_index, batch_actions in enumerate(actions):
for action_index, action in enumerate(batch_actions):
action_mapping[(batch_index, action_index)] = action[0]
for i in range(batch_size):
# gold sql exactly as given
original_gold_sql_query = ' '.join(world[i].get_query_without_table_hints())
if i not in best_final_states:
self._exact_match(0)
self._action_similarity(0)
self._sql_evaluator_match(0)
self._acc_multi(0)
self._acc_single(0)
continue
best_action_indices = best_final_states[i][0].action_history[0]
action_strings = [action_mapping[(i, action_index)]
for action_index in best_action_indices]
predicted_sql_query = action_sequence_to_sql(action_strings, add_table_names=True)
if target_list is not None:
targets = target_list[i].data
sequence_in_targets = self._action_history_match(best_action_indices, targets)
self._exact_match(sequence_in_targets)
sql_evaluator_match = self._evaluate_func(original_gold_sql_query, predicted_sql_query, world[i].db_id)
self._sql_evaluator_match(sql_evaluator_match)
similarity = difflib.SequenceMatcher(None, best_action_indices, targets)
self._action_similarity(similarity.ratio())
difficulty = self._query_difficulty(targets, action_mapping, i)
if difficulty:
self._acc_multi(sql_evaluator_match)
else:
self._acc_single(sql_evaluator_match)
beam_hit = False
for pos, final_state in enumerate(best_final_states[i]):
action_indices = final_state.action_history[0]
action_strings = [action_mapping[(i, action_index)]
for action_index in action_indices]
candidate_sql_query = action_sequence_to_sql(action_strings, add_table_names=True)
if target_list is not None:
correct = self._evaluate_func(original_gold_sql_query, candidate_sql_query, world[i].db_id)
if correct:
beam_hit = True
self._beam_hit(beam_hit)
def _get_sql(self, initial_state, actions):
num_steps = self._max_decoding_steps
best_final_states = self._beam_search.search(num_steps,
initial_state,
self._transition_function,
keep_final_unfinished_states=True)
batch_size = len(actions)
predicted_sql_query_list = []
best_action_indices_list = []
action_mapping = {}
for batch_index, batch_actions in enumerate(actions):
for action_index, action in enumerate(batch_actions):
action_mapping[(batch_index, action_index)] = action[0]
for i in range(batch_size):
if i not in best_final_states:
self._exact_match(0)
self._action_similarity(0)
self._sql_evaluator_match(0)
self._acc_multi(0)
self._acc_single(0)
predicted_sql_query_list.append('')
continue
best_action_indices = best_final_states[i][0].action_history[0]
best_action_indices_list.append(best_action_indices)
action_strings = [action_mapping[(i, action_index)]
for action_index in best_action_indices]
predicted_sql_query = action_sequence_to_sql(action_strings, add_table_names=True)
predicted_sql_query_list.append(predicted_sql_query)
return predicted_sql_query_list, best_action_indices_list
